For the imbalanced support set experiments, we used MiniImageNet [1]}, [2]} following the same version popular version as [2]}, [4]}. All meta-learning models used 64, 16, 20 classes for the meta-training \(\mathcal {D}_{train}\) , meta-validation \(\mathcal {D}_{val}\) , and meta-testing \(\mathcal {D}_{test}\)  datasets, respectively, with each class containing 600 samples. All images are resized to 84 by 84px. For the feature-transfer baselines (1-NN, fine-tune, and Baseline++), we used a conventionally partitioned training and validation datasets. Specifically, we combined \(\mathcal {D}_{train}\)  and \(\mathcal {D}_{val}\)  classes (i.e., \(64 + 16 = 80\)  classes), then partitioned the samples of each class into 80% - 20% split for pre-training - validation, forming \(\mathcal {D}^{\prime }_{train}\)  and \(\mathcal {D}^{\prime }_{val}\)  (where \(\mathcal {D}^{\prime (y)}_{train} \equiv \mathcal {D}^{\prime (y)}_{val}\) ). Thus, the baseline were trained on the same number of training samples as the meta-learning methods, albeit with more classes and less samples per class. All models were evaluated on FSL tasks sampled from \(\mathcal {D}_{test}\) .
